We present a simple, yet general method to detect fake videos displaying human subjects, generated via Deep Learning techniques. The method relies on gauging the complexity of heart rate dynamics as derived from the facial video streams through remote photoplethysmography (rPPG). Features analyzed have a clear semantics as to such physiological behaviour. The approach is thus explainable both in terms of the underlying context model and the entailed computational steps. Most important, when compared to more complex state-of-the-art detection methods, results so far achieved give evidence of its capability to cope with datasets produced by different deep fake models.

DeepFakes Have No Heart: A Simple rPPG-Based Method to Reveal Fake Videos / G. Boccignone, S. Bursic, V. Cuculo, A. D'Amelio, G. Grossi, R. Lanzarotti, S. Patania (LECTURE NOTES IN COMPUTER SCIENCE). - In: Image Analysis and Processing – ICIAP 2022 / [a cura di] S. Sclaroff, C. Distante, M. Leo, G.M. Farinella, F. Tombari. - [s.l] : Springer, 2022. - ISBN 978-3-031-06429-6. - pp. 186-195 (( convegno International Conference on Image Analysis and Processing, ICIAP 2021 tenutosi a Lecce nel 2022 [10.1007/978-3-031-06430-2_16].

DeepFakes Have No Heart: A Simple rPPG-Based Method to Reveal Fake Videos

G. Boccignone;S. Bursic;V. Cuculo;A. D'Amelio
;
G. Grossi;R. Lanzarotti;S. Patania
2022

Abstract

We present a simple, yet general method to detect fake videos displaying human subjects, generated via Deep Learning techniques. The method relies on gauging the complexity of heart rate dynamics as derived from the facial video streams through remote photoplethysmography (rPPG). Features analyzed have a clear semantics as to such physiological behaviour. The approach is thus explainable both in terms of the underlying context model and the entailed computational steps. Most important, when compared to more complex state-of-the-art detection methods, results so far achieved give evidence of its capability to cope with datasets produced by different deep fake models.
DeepFake detection; rPPG; Image forensics; Biological signals
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/930968
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